Applied AI Engineer

Who are we Fulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services right from ideation to implementation. These services have applicability across a variety of industries, including banking & financial services, insurance, retail, higher education, food, healthcare, and manufacturing. Role Summary The Mid-Senior Applied AI Engineer is responsible for designing, developing, and deploying scalable artificial intelligence solutions within an enterprise AI platform. Moving beyond basic implementation, this role requires architectural thinking to build robust GenAI services, complex retrieval pipelines (RAG), and agentic workflows. The engineer will partner closely with AI Program Management, Data & Analytics, and business stakeholders to translate validated business requirements into secure, production-ready AI applications. Key Responsibilities Platform Development: Design and build core AI application components supporting enterprise AI initiatives, including agentic workflows, batch processing, and data integrations. Advanced Retrieval & Agentic Architectures: Implement and optimize sophisticated embeddings, vector search strategies, and multi-agent workflows to handle both text and structured data retrieval from core enterprise systems. API & Backend Engineering: Develop secure, high-performance backend APIs (primarily FastAPI/Python) to facilitate seamless integration between foundational models and internal enterprise architecture. Model Integration & Orchestration: Work with multiple LLMs (OpenAI, Claude, Gemini) via model-agnostic routing layers, optimizing for cost, latency, and task-specific performance. Feasibility & Scoping: Collaborate directly with AI Program Managers and Business Analysts during the intake phase to assess technical feasibility, architecture requirements (e.g., Agentic vs. Standard ML), and data readiness for new business requests. Deployment & LLMOps: Drive the deployment of AI systems, establishing CI/CD pipelines, containerization, and robust LLM monitoring (observability, prompt drift, and accuracy metrics). Governance & Compliance: Ensure all AI components strictly adhere to enterprise AI governance, security, and data privacy standards. Mentorship: Provide technical guidance and code reviews for junior developers on the team. Required Technology Stack AI Models: Deep familiarity with API integration and prompting for OpenAI, Anthropic (Claude), and Google (Gemini) models. Frameworks: Advanced proficiency in LangChain, LangGraph, and LlamaIndex for building RAG pipelines and agentic decisioning systems. Backend & APIs: Strong expertise in Python and FastAPI. Basic working knowledge of Node.js. Data Architecture: Experience designing schemas and optimizing queries for Vector Databases (e.g., Azure AI Search, Pinecone, Milvus) and relational databases (PostgreSQL). Cloud Infrastructure: Hands-on experience with Azure Cloud and Azure AI Services. DevOps & Deployment: Proficiency with Git, Docker, and CI/CD principles. Experience with LLM observability tools is highly preferred.